Detecting fraud by calculating email address prefix mean keyboard distances using machine learning optimization

Pending Publication Date: 2021-09-23
INTUIT INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0010]Details of one or more implementations of the subject matter described in this disclosure are set forth in the accompanying drawings and the description below. Other features,

Problems solved by technology

As a result, fraudulent parties (referred to as “fraudsters”) can use fake email addresses to create synthetic email accounts that are

Method used

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  • Detecting fraud by calculating email address prefix mean keyboard distances using machine learning optimization
  • Detecting fraud by calculating email address prefix mean keyboard distances using machine learning optimization
  • Detecting fraud by calculating email address prefix mean keyboard distances using machine learning optimization

Examples

Experimental program
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Embodiment Construction

[0020]Various implementations of the subject matter disclosed herein relate generally to a fraud detection system for identifying fraudulent email addresses associated with an electronic payment service. The fraud detection system can classify an email address as suspicious or not suspicious based on calculating keyboard distances between characters in a prefix of each email address as input into the fraud detection system (such as to access the electronic payment service). As used herein, the prefix of an email address refers to the alphanumeric characters of the email address that precede the “@” symbol. For example, the prefix of the email address “firstname.lastname.@domain.com” is “firstname.lastname.”

[0021]Some implementations more specifically relate to fraud detection systems that receive an email having a prefix of a certain length (referred to herein as the “prefix length”), and identify each of a number of bigrams within the prefix, for example, where each bigram consists...

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PUM

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Abstract

This disclosure relates to systems and methods for identifying fraudulent email addresses associated with an electronic payment service. In some implementations, a computing device receives an email with a prefix having a number of characters and characterized by a prefix length indicative of the number of characters in the prefix. The computing device identifies each of a number of bigrams is identified within the prefix, and determines a row and column distance for each bigram between two consecutive characters of the bigram as positioned on a keyboard. The computing device calculates a Euclidean distance between the two consecutive characters of the bigram based on the row and column distances, and determines a normalized distance based on the prefix length and an average of the Euclidean distances calculated for the number of bigrams in the prefix. The normalized distance is compared with a value to classify the email as suspicious or as not suspicious.

Description

TECHNICAL FIELD[0001]This disclosure relates generally to detecting fraud associated with an electronic payment service, and more specifically, to identifying fraudulent email addresses associated with an electronic payment service.DESCRIPTION OF RELATED ART[0002]Creating an account for an electronic payment service may not require a new user to provide a valid email address. As a result, fraudulent parties (referred to as “fraudsters”) can use fake email addresses to create synthetic email accounts that are not attributable to real users, and then use the fake email addresses to create fraudulent accounts with the electronic payment service. It would be desirable for an electronic payment service to quickly and accurately detect these fake email addresses, which in turn may lead to the identification of fraudulent accounts created in the electronic payment service.SUMMARY[0003]This Summary is provided to introduce in a simplified form a selection of concepts that are further descri...

Claims

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Application Information

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IPC IPC(8): G06N5/04G06N20/00G06Q10/10G06Q10/04G06Q30/00G06K9/62
CPCG06N5/04G06N20/00G06K9/6248G06Q10/04G06Q30/0185G06Q10/107H04L51/212H04L51/48G06N5/01G06F18/21355
Inventor EYAL ALTMAN, NOAHBASSON, ORRESHEFF, YEHEZKEL SHRAGAHORESH, YAIR
Owner INTUIT INC
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